Abstract
In present days, several cloud computing platforms or web services such as Flip kart and Amazon, Google App Engine, blue cloud etc. provide a locally distributed and scalable data which is in uncountable form. But, these platforms do not regard geographical location data. However, the data is generated from the modern remote satellites with their geological topology. The so obtained geo-distributed database is able to process either a large scale data or a very simple type, scalable while being fault-tolerant and fast in answering a query. The processing of Big data includes the storing and analysing the uncountable amount of geographical data. The big data processing utilizes several programming models and frameworks such as Map Reduce, Hadoop, MongoDB, Pig etc. The present work concentrates on land use classification of various cities in India using geographical location data having latitude and longitude of every boundary. To perform this work, India map shape file with every state is used. The shape file is converted into longitude and latitude band information along with cities data. Nevertheless, the geo-graphical data is classified by applying the machine learning algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
T. Sarath, G. Nagalakshmi, An land cover fuzzy logic classification by maximum likelihood. Int. J. Comput. Trends Technol. (IJCTT) 13(2) (2014) arXiv:1407.4739
G. Nagalakshmi, T. Sarath, An SVM fuzzy logic classification for land cover. Int. J. Manag., Technol. Eng. (2018). ISSN NO: 2249-7455
M. Sirish Kumar, B. Kavitha, S. Jyothi, G. Nagalakshmi, Land use/land cover of Tirupati area for agriculture land classification: a study. Int. J. Eng. Res. Comput. Sci. Eng. (IJERCSE) 5(4) (2018)
T. Sarath, G. Nagalakshmi, S. Jyothi, A study on hyperspectral remote sensing classification. Int. J. Comput. Appl. (2015), in International Conference on Information and Communication Technologies (2014). doi: 0975-8887
K. SreeDivya, P. Bhargavi, S. Jyothi, Machine learning algorithms in big data analytics. Int. J. Comput. Sci. Eng. 6(1), 63–70 (2018). E-ISSN. 2347-2693
J. Jeong, Three Major Factors for a Successful Big Data Application (National Information Society Agency, Seoul, 2012)
K. Song, Understanding Society Through SOCIAL Metrics (Daum Soft, Seoul, 2011)
B. Saha, D. Srivastava, Data quality: the other face of big data, in Proceedings of the 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 1294–1297 (2014)
D. Zielstra, A. Zipf, A comparative study of proprietary geodata and volunteered geographic information for Germany, in Proceedings of the 13th AGILE International Conference on Geographic Information Science (2010)
L. Zhang, A. Stoffel, M. Behrisch, S. Mittelstadt, T. Schreck, R. Pompl, D. Keim, Visual analytics for the big data era—a comparative review of state-of-the-art commercial systems, in Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 173–182 (2012). https://doi.org/10.1109/vast.2012.6400554
A. Tanuja, D. Swetha Ramana, Processing and analyzing big data using Hadoop. Int. J. Comput. Sci. Eng. 4.4, 91–94 (2016)
X. Gao, J. Cai, Optimization analysis of urban function regional planning based on big data and GIS technology. Techn. Bull. 55(11), 344–351 (2017)
B. Shneiderman, The big picture for big data: visualization. Science 343(6172), 730 (2014). https://doi.org/10.1126/science.343.6172.730-a
The Flow of Human Migration [Internet] (2018). Available from: http://www.public.tableau.com. Accessed: 1 Sep 2018
D. Huffman, On the abuse of Chernoff faces. Catastrophe [Internet]. 2010. Available from http://cartastrophe.wordpress.com/2010/06/16/on-theabuse-of-chernoff-faces. Accessed: 2 Sep 2018
N.S. Altman, An introduction to kernel and Nearest-Neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992). https://doi.org/10.1080/00031305.1992.10475879
L. Kozma, k Nearest Neighbours Algorithm (Helsinki University of Technology, 2008). Available: http://www.lkozma.net/knn2.pdf
E. Mirkes, KNN and Potential Energy (Applet) (University of Leicester, 2011). Available: www.math.le.ac.uk/people/ag153/homepage/KNN/KNN3.html
F. Maselli, G. Chirici, L. Bottai, P. Corona, M. Marchetti, Estimation of mediterranean forest attributes by the application of K-NN procedures to multi-temporal landsat ETM+Images. Int. J. Rem. Sens. 26(17), 3781–3796 (2005). https://doi.org/10.1080/01431160500166433
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
VinilaKumari, S., Bhargavi, P., Jyothi, S. (2020). Identification of Neighbourhood Cities Based on Landuse Bigdata Using K-Means and K-NN Algorithm. In: Jyothi, S., Mamatha, D., Satapathy, S., Raju, K., Favorskaya, M. (eds) Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-46939-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-46939-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46938-2
Online ISBN: 978-3-030-46939-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)